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A Survey on Quantum Computing for Recommendation Systems

Giovanni Pilato, Filippo Vella

Published Dec 29, 2022• 21 citations
Publisher

Abstract

Recommendation systems play a key role in everyday life; they are used to suggest items that are selected among many candidates that usually belong to huge datasets. The recommendations require a good performance both in terms of speed and the effectiveness of the provided suggestions. At the same time, one of the most challenging approaches in computer science is quantum computing. This computational paradigm can provide significant acceleration for resource-demanding and time-consuming algorithms. It has become very popular in recent years, thanks to the different tools available to the scientific and technical communities. Since performance has great relevance in recommendation systems, many researchers in the scientific community have recently proposed different improvements that exploit quantum approaches to provide better performance in recommendation systems. This paper gives an overview of the current state of the art in the literature, outlining the different proposed methodologies and techniques and highlighting the challenges that arise from this new approach to the recommendation systems domain.

DOI
10.3390/info14010020

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Recommendation systems play a key role in everyday life; they are used to suggest items that are selected among many candidates that usually belong to huge datasets. The recommendations require a good performance both in terms of speed and the effectiveness of the provided suggestions. At the same time, one of the most challenging approaches in computer science is quantum computing. This computational paradigm can provide significant acceleration for resource-demanding and time-consuming algorithms. It has become very popular in recent years, thanks to the different tools available to the scientific and technical communities. Since performance has great relevance in recommendation systems, many researchers in the scientific community have recently proposed different improvements that exploit quantum approaches to provide better performance in recommendation systems. This paper gives an overview of the current state of the art in the literature, outlining the different proposed methodologies and techniques and highlighting the challenges that arise from this new approach to the recommendation systems domain.
The field of Quantum Computing (QC) has gained significant popularity in recent years, due to its potential to provide benefits in terms of efficiency and effectiveness when employed to solve certain computationally intensive tasks. In both Information Retrieval (IR) and Recommender Systems (RS) we are required to build methods that apply complex processing on large and heterogeneous datasets, it is natural therefore to wonder whether QC could also be applied to boost their performance. The tutorial aims to provide first an introduction to QC for an audience that is not familiar with the technology, then to show how to apply the QC paradigm of Quantum Annealing (QA) to solve practical problems that are currently faced by IR and RS systems. During the tutorial, participants will be provided with the fundamentals required to understand QC and to apply it in practice by using a real D-Wave quantum annealer through APIs.
Advancements in computer technologies have been evolving in order to provide new services or to improve recent computing performance. Applications of computer technologies to several real-world domains are becoming more realistic and efficient. Two challenges are taken into consideration when applying such technologies including more complicated user requirements and exponential growth of data. In the real world, which is driven by business, achieving user requirements is one promising way of yielding competitive advantages. Moreover, time constraints are also crucial for corporate users. Apart from structured data, unstructured data have been significantly generated via several platforms. Such aspects make data processing more demanding. Quantum technology, which is instantiated in physics, has been utilized in various fields including computer science. Even the real quantum computer is still in its early development, but other concepts such as quantum information and quantum computing are making progress. Furthermore, some quantum-based algorithms have been analytically proven to have higher performance compared to the traditional ones. According to such important milestones, quantum computing has been regarded as one of the key infrastructures of future computing. This paper aims to focus on the main aspects of quantum computing including its backgrounds and recent advancement to provide a clearer picture. Its recent applications to intelligent system enhancement, cybersecurity and cryptography empowerment, and finance are addressed. Several key ideas, and opportunities of quantum computing utilization are also outlined based upon systematic reviews of existing works and case studies. Moreover, comparative analysis between classical and quantum computing in terms of core principles, computational efficiency, and practical feasibility is given. Better understanding and proper preparation of applying quantum computing are thus achieved.
In recent years, Quantum Computing witnessed massive improvements in terms of available resources and algorithms development. The ability to harness quantum phenomena to solve computational problems is a long-standing dream that has drawn the scientific community’s interest since the late ’80s. In such a context, we propose our contribution. First, we introduce basic concepts related to quantum computations, and then we explain the core functionalities of technologies that implement the Gate Model and Adiabatic Quantum Computing paradigms. Finally, we gather, compare, and analyze the current state-of-the-art concerning Quantum Perceptrons and Quantum Neural Networks implementations.
In the world of the digital domain, personalized recommendation systems plays an important role in enriching user experiences. Though traditional approaches like collaborative and content-based filtering are universally used, they face challenges such as data insufficiency and initialization issues for new users. This study examines the combining of Graph based reinforcement learning and Quantum Computing to deal with these challenges. Graph based reinforcement learning for the personalized recommendation system can implement connections between the users and selected the items, represented as nodes or edges, to get complex relationships and enrich recommendations. Reinforcement Learning can help in data-driven training on a concurrent basis. Merging Quantum Computing with this provides a breakthrough in optimization and cognitive abilities. By utilizing quantum mechanics principles, quantum computing can optimize the balance between exploration and exploitation, producing prompt convergence to optimal recommendation techniques.
After decades of being mainly confined to theoretical research, Quantum Computing is now becoming a useful tool for solving realistic problems. This work aims to experimentally explore the feasibility of using currently available quantum computers, based on the Quantum Annealing paradigm, to build a recommender system exploiting community detection. Community detection, by partitioning users and items into densely connected clusters, can boost the accuracy of non-personalized recommendation by assuming that users within each community share similar tastes. However, community detection is a computationally expensive process. The recent availability of Quantum Annealers as cloud-based devices, constitutes a new and promising direction to explore community detection, although effectively leveraging this new technology is a long-term path that still requires advancements in both hardware and algorithms. This work aims to begin this path by assessing the quality of community detection formulated as a Quadratic Unconstrained Binary Optimization problem on a real recommendation scenario. Results on several datasets show that the quantum solver is able to detect communities of comparable quality with respect to classical solvers, but with better speedup, and the non-personalized recommendation models built on top of these communities exhibit improved recommendation quality. The takeaway is that quantum computing, although in its early stages of maturity and applicability, shows promise in its ability to support new recommendation models and to bring improved scalability as technology evolves.